1. Company profile

Shenzhen Semet New Materials Co., Ltd.(hereinafter referred to as "Semet") was established in 2016 and is headquartered in Pingshan, Shenzhen. It is a national high-tech enterprise and a specialized and new enterprise in Shenzhen. The company is deeply involved in the fields of suspension smelting and suspension metallurgy technology, and is one of the few high-end equipment manufacturers in China with the ability to independently develop ultra-high temperature smelting equipment.

Semmet's core business focuses on three major sectors: first, it develops high-temperature smelting equipment by itself. The maximum working temperature of its suspension smelting furnace reaches 3600 ° C. Its product line covers suspension smelting furnaces, vacuum arc furnaces, plasma smelting furnaces, centrifugal ingot equipment, etc.; Second, special metal new material development and processing services, providing customized smelting and testing services for high-end materials such as high-purity titanium, high-purity nickel, and rare earth alloys; The third is collaborative innovation between industry, academia and academia, and the joint laboratory for new materials has been jointly established with Harbin Institute of Technology to continue to promote the engineering application of suspension metallurgy technology.

The company's core customer base covers three types of entities: top universities and scientific research institutes such as Tsinghua University and Chinese Academy of Sciences; strategic emerging companies in fields such as high-purity materials for semiconductors and high-temperature energy storage alloys; and special materials in high-end equipment manufacturing fields such as aerospace. supplier. With the integrated model of "equipment + materials + services", Semet has solved industry pain points such as impurity pollution, component segregation, and difficulty in forming highly active metals in traditional smelting processes, and has a firm position in the domestic suspension smelting equipment segment. At the forefront.

2. Multiple promotion dilemmas through traditional channels

The ultra-high temperature smelting equipment and special metal materials industry in which Semet is located has the distinctive characteristics of "extremely scattered customers, long procurement decision-making chain, and extremely high threshold of technical trust." Its market expansion has long faced three structural constraints:

1. Insufficient accuracy in reaching target customers. The annual procurement demand for suspension smelting furnaces, electric arc furnaces and other equipment is highly concentrated on specific scenarios such as university laboratory construction, platform upgrades of scientific research institutes, and expansion of production lines of new material companies. The promotion of traditional B2B platforms is like "finding a needle in a haystack." Semmet has invested a lot of budget in bidding on general search engines, but among the visitors attracted by keywords such as "smelting furnace" and "electric arc furnace", less than 5% of effective customers truly have ultra-high temperature suspension smelting technology awareness and procurement budget. A large amount of traffic is consumed by demanders of mid-to-low-end heat treatment equipment, and the cost of obtaining customers remains high.

2. The efficiency of technical value transmission is low. The core advantages of suspension smelting technology-no crucible pollution, precise temperature control by electromagnetic suspension, and stable melting and casting of highly active metals-belong to a highly specialized knowledge field. Traditional graphic advertisements and product manuals are difficult to establish customers 'technical trust in a short period of time. The sales team frequently encounters the cognitive gap that "customers know they need smelting equipment, but are not sure whether they need suspension smelting technology." The period from initial contact to confirmation of technical solutions often lasts as long as 3 to 6 months, and a large number of potential needs are lost during the long education process.

3. Migration of decision-making habits of customers in scientific research institutes. Semmet's core customer groups-professors at the School of Materials of universities, project leaders of scientific research institutes, and R & D directors of semiconductor companies-are increasingly inclined to directly report to ChatGPT, Wenxinyiyan, Kimi and other large models during the equipment selection and research stage. tool initiates in-depth technical consultation. Typical questions include "What is the difference between suspension smelting furnaces and vacuum induction furnaces","Which domestic company can do 3600-degree ultra-high temperature smelting","How to avoid oxygen pollution in high-purity titanium smelting", etc. These Q & A scenarios have become the "first entry point" in the customer's decision-making chain. However, Semmet's brand and technological advantages have not yet formed an effective place in this emerging channel. Instead, they have been covered by some international competing products or second-hand content with inaccurate information.

The deeper anxiety lies in the fact that a single suspension smelting equipment is often worth millions, and customers must conduct sufficient technical verification and supplier credit investigation before purchasing. If the large model fails to quote Semmet's authoritative information when answering relevant technical questions, the company may even be excluded from the candidate list at the "starting point" of customer search behavior, and the subsequent follow-up by the traditional sales team will be meaningless.

3. Binshang customizes GEO optimization periodic plans based on the problems faced and the specificities of the industry in which the company is located

After in-depth research on Semmet's business structure, the Binshang GEO service team identified special difficulties in GEO optimization in this industry: the large model has a shallow understanding of subdivided technical concepts such as "suspension smelting" and "suspension metallurgy", and domestic enterprises with equipment manufacturing capabilities in this field are sparsely distributed in the training data, and there is a significant semantic gap between the professional expression of technical parameters and the customer's verbal questions. Based on this, the team tailor-made a three-stage cyclical plan for Semmet of "technical concept anchoring-knowledge map construction-question and answer scene blocking".

Phase 1 (Weeks 1 to 4): Anchoring technical concepts and structuring knowledge assets. Binshang first sorted out Semmet's technical asset system and transformed the core parameters of the suspension smelting furnace, the scientific research results of the laboratory co-constructed with Harbin Institute of Technology, and typical cases of serving semiconductor and energy storage customers into a standardized QA format that is easy to interpret large models. Focusing on 35 high-frequency technical questions such as "Working Principles of Suspension Smelting Furnace","Selection of 3600-degree Ultra-high Temperature Smelting Equipment" and "Advantages of High-purity Titanium Suspension Smelting Process", structured question and answer content are produced, which are deployed in the technical column of the company's official website, Zhihu institution number, scientific research community and academic platform. Synchronously implement Schema tag optimization to ensure that key information such as equipment model, technical parameters, patent qualifications, and cooperative institutions can be accurately captured and quoted by the large model.

Phase 2 (Weeks 5 to 10): Vertical scene penetration and semantic alignment optimization. In response to the colloquial and situational characteristics of target customers when asking questions about large models, Binshang plans a series of in-depth technical contents, including "Application of Suspension Smelting Technology in the Preparation of Semiconductor High-purity Materials" and "Why Suspension Smelting is the First Choice for Energy Storage Superalloys" 15 professional articles such as Process "and" Guide to Selection of Suspension Smelting Equipment in University Laboratory ". The content creation adopts the strategy of "restoring the customer's original words", and the title and body are actively embedded in high-frequency questions received by large models such as "Which is better for suspension smelting furnace","Domestic Suspension Metallurgical Equipment Manufacturers Ranking" and "Which is better for high-purity metal smelting". While answering technical questions, the cognitive correlation of "Semet equals a leading brand in suspension smelting technology" is naturally established. At the same time, the team operates accounts of scientific research community experts such as Zhihu and Xiaolu, and participates in discussions on topics such as suspension metallurgy and superalloys to enhance the brand's reference weight in the technical community.

The third stage (weeks 11 to 16): Question and answer occupancy monitoring and authoritative source strengthening. Binshang has established a large model question and answer tracking system to regularly feed test questions to mainstream AI tools such as ChatGPT, Wenxinyiyan, Tongyi Qianwen, Kimi, and monitor the answer composition and brand citations of core queries such as "Suspension smelting furnace manufacturers recommended" and "Domestic suppliers of ultra-high temperature smelting equipment". In view of the fact that large models are not cited, the cited information is lagging behind, or the parameters are inaccurate, the E-E-A-T score of the Semet brand is strengthened through collaborative publication of academic journals, joint release of industry white papers, and endorsement of laboratory results of Harbin Institute of Technology. At this stage, traditional customer cases are simultaneously rewritten into the narrative structure of "problem-technical challenge-solutions-quantitative results" preferred by the large model, increasing the probability that the content will be cited by generative answers.

4. The effect of comprehensive customer acquisition and improvement after GEO is implemented

After more than four months of cyclical optimization operations, Semet's brand visibility and customer acquisition efficiency among scientific research institutes and high-end manufacturing companies have achieved a systematic leap, with online exposure, inquiry quality, brand awareness, and customer service efficiency. Significant improvements have been achieved.

Online exposure dimensions: The visibility of the Semmet brand and related technologies in the mainstream large model Q & A has increased from less than 2% before optimization to 41%. The answers to core categories such as "suspension smelting furnace","ultra-high temperature smelting equipment" and "high-purity titanium smelting" have steadily entered the top three. Among the natural traffic on the official website, the proportion of visits from long-tail words in technical questions and answers jumped from 9% to 34%, and the average monthly UV increased by 182%. Moreover, visitors 'stay time and page depth were significantly better than traditional channels.

Enquiry quality dimension: The customer consultation brought by the GEO channel is highly accurate. Customers from universities and scientific research institutes account for 55%, and the proportion of inquiries that explicitly mention specific technical parameters or application scenarios reaches 63%. The average technical confirmation cycle has been reduced from 4 months to 6 weeks. In the first quarter of 2024, the intended transaction amount of equipment inquiries from GEO sources exceeded 8 million yuan, and the transaction conversion rate was 2.7 times that of industry exhibition channels.

Brand awareness dimension: Semmet's online searches in the suspension smelting and special metallurgy segments increased by 112% month-on-month, and user discussions in scientific research forums and technical communities that actively mentioned the Semmet brand increased fourfold. What is more strategic is that Semet has been directly recommended to terminal inquiry users by several large models as "the main supplier of domestic suspension smelting equipment" and "the representative enterprise of high-purity titanium suspension smelting technology", forming the automated association of "Suspension Smelting Technology Inquiries-The emergence of the Semet brand", and the brand's mental occupancy effect has initially emerged.

Customer service efficiency dimension: The construction of a structured technical Q & A knowledge base allows sales engineers to submit standardized questions such as "The difference between suspension smelting and induction smelting" and "Equipment temperature control accuracy parameters" to online content self-service answers, and the team focuses on Customized solution design and in-depth technical negotiation. The time limit for customers 'first technical response was shortened from 72 hours to 8 hours, the plan delivery cycle was reduced by an average of 35%, the customer satisfaction score was increased to 4.9 points, and the proportion of repurchases and reintroductions by old customers increased simultaneously.

Semet's practice has profoundly demonstrated that GEO optimization has special value for technology-intensive B2B equipment manufacturing companies with highly vertical customer bases. When the starting point of decision-making for target customers shifts from search engines to large model Q & A, whether the company can occupy a credible reference position in the AI-generated answers directly determines whether it can enter the subsequent procurement evaluation list. Through pre-positioned knowledge asset layout and scenario-based content semantic alignment, Semet has successfully transformed its technical advantages into brand potential energy in the large model channel, opening up a replicable evolution path for digital customer acquisition in the high-end metallurgical equipment industry.

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